In the last article, we have discussed GraphQL advantages over REST. In this article, we will see GraphQL in action. I have created a sample application to showcase differences between REST and GraphQL. First, we will see the REST implementation of simple product detail endpoint. I have used Spring Boot to demonstrate REST. Download sample project and follow the steps outlined in README to set up the project. I am not discussing setup details here as it is out of scope for this article. Assuming that your project is up and running to make a call to http://localhost:8080/product/{product_id} endpoint to get product detail JSON as shown below.

If you observe above JSON, we are getting entire product JSON including reviews and technical specifications though we are not interested in all the elements of a given product.

Now we will see GraphQL in action by getting product details in a selective manner. To demonstrate GraphQL again I used Spring Boot. Download sample project and follow the steps outlined in README to set up the project. I am not discussing setup details here as it is out of scope for this article. Assuming that your project is up and running to see GraphQL in action. In this case, I am interested to get only product id, title, short description and list price of a given product. Let us see how we can query to get interesting details.

Now as a service consumer I am interested to get product id, title, short description, list price, and reviews. In this case, GraphQL gives the flexibility to query what we want. See below query and response when we use GraphQL.

To demonstrate GraphQL I have used GUI based plugin GraphiQL. For consuming from other applications we can configure endpoint in application.properties.

Now we can make a call to the above endpoint by passing URL encoded query parameter as shown below. You can learn more about query and mutations https://graphql.org/learn/queries/

Hope you enjoyed this article. I will come back with another article. Till then, Happy Learning!!!

In this article let us explore GraphQL. Let us first understand what GraphQL is? GraphQL is a specification from Facebook. GraphQL is a query language for APIs and runtime for fulfilling those queries with your existing data. GraphQL gives clients the power to ask for exactly what they need and nothing more by avoiding over fetching or under fetching of data. We can understand it more when we are going to see GraphQL implementation in action. Till then hold your curiosity.

Wait, Wait… So far we are using REST(Representation State Transfer) to expose our services as APIs. Let us ask some questions ourselves before getting deeper into GraphQL.

Why do I need to adopt GraphQL?What problems am I facing with REST APIs? How GraphQL solves those? To answer the above queries let us take a use case to build an e-commerce application for web, mobile and native clients. We decided to expose APIs for various e-commerce functionalities. For example, I have product detail REST API which gives specific product information as JSON which includes product data attributes, specifications data, reviews data, etc. As we are having many attributes in product JSON, the size of it is more. Each client (web and thin clients(mobile and tablets)) has it’s own front end requirements to display product data as they have different screen sizes, memory, network bandwidth, etc. Now my clients started consuming product detail API. Though mobile and tablet interfaces don’t require entire product JSON as web, still product detail API is giving entire product data. It is evident that clients don’t have control over the data what they want from the server. This is called over fetching. The pictorial representation of REST over fetching issue is given below. We can solve over fetching issue with various approaches. The straight forward approach is to maintain different APIs for thick and thin clients. Though this design solves over fetching issue but has other problems like code maintenance, implementation of enhancements across different APIs, deployment of thick, thin client APIs, more compute, more manpower, etc., which puts more cost to project. The other approach is having middleware to intercept the client request. Based on client request filter the response to return. This adds an additional layer to the application which has the same issues as the previous approach.

Now let us discuss the second issue with REST called under fetching. To avoid over fetching, we decided to create granular APIs so that clients will make API calls for whichever the data they required. Let us take a product detail page for the web. It has product information, specifications and reviews to display. Now to render product detail page client is not going to get data in a single API call. So client needs to make multiple API calls (like basic product API, specification API, Reviews API) to cater to its data requirement. This design has performance issues with an increased number of round trips to the backend server and APIGateway. The other issue is requiring more computing power and network as rising in the number of requests to serve. Below is a pictorial representation of under fetching. Let us see the third issue with REST that is, evolving APIs with versions. Any API will evolve as business needs will change with time. As per our customer needs, we might need to add data attributes(most of the cases we won’t remove data attributes as we need to have backward compatibility) to existing APIs. When we do any changes to existing APIs, we need extra vigilant as the changes might break the clients. To avoid that we will do versioning of APIs as and when we plan to release changes to existing APIs. When we introduce new versions which put the burden of managing more APIs(i.e. more compute power, more manpower), planning to deprecate older versions. Discipline and communication are needed when we have more versions of an API. With REST we cannot do silent releases.

The above issues are leading us to look for another solution called GraphQL. We will see how GraphQL addresses the above-said issues by implementing an API in the upcoming article. Meanwhile, let us see the request and response paradigm with GraphQL and how GraphQL makes clients happy by serving what they want. Here are some of the adopters of GraphQL https://graphql.org/users/.
In the coming article, we will see the implementation of an API with GraphQL. Till then Spread love for APIs!!!

In this article, we will see how to create a CI/CD pipeline for APIGEE API proxies. I have referred a couple of articles on APIGEE community on the same topic. Those gave some idea on how to setup CI/CD pipeline for API proxies. Here are the tools which I have used to setup CI/CD.

Make sure that you have created APIGEE edge account and a sample proxy to start with. Below is the architecture diagram which shows the CI/CD pipeline and the stages involved. You can use this as a baseline CI/CD for your projects and can enhance it based on your requirements.

Here are the steps I have implemented in CI/CD pipeline.

Developer pushes the API proxy code to GIT.

Jenkins polls GIT and starts CI/CD Stage 1 based on GIT changes.

As part of Stage 1, the code will be pulled into the workspace.

In “Static Code Analysis” stage, the code will be analyzed for any violations of best code practices and anti-patterns usage. If this stage is the success it proceeds with the build stage. After each stage completion either success or failure, the notification will be sent to Slack channel.

As part of the build stage, we will create APIGEE API proxy bundle.

In the Deploy stage, I used APIGEE management APIS to deploy the API proxy bundle.

Once the deployment is successful, then the integration tests will be triggered. I used Newman to do integration tests. Newman requires integration tests collection file as input. The test cases can be created easily with Postman

In all the stages the notifications will be triggered to Slack channel.

There are some enhancements which I will do in the coming days. Below are some of the changes which I will target as enhancements.

Adding email, Hipchat notifications

Revert the API proxy to a previous revision if the integration tests fail.

In this article, we will see how we can manage logs generated by APIGEE API gateway with Elastic Search, Logstash and Kibana. The below diagram highlights how log data flows through ELK stack for data visualization and monitoring.

As part of the API gateway, we will use MessageLogging policy to capture API proxy logs at proxy flow, target flow and post client flow based on the requirement. As of today, MessageLogging policy supports Syslog and file-based logging(available for only on-premise installations). Below is MessageLogging policy configuration to send logs to the syslog server.

Now we will see logstash configuration to ingest data from syslog to logstash. Here I am not giving steps to setup ELK as that is out of scope.

Once Syslog data is ingested successfully to Logstash we can configure visualization based on available fields in the index. Below is sample visualization created for API proxy response status codes.

In coming articles we will discuss another topic. Till then, Spread love for APIs!!!

APIGEE sits between service consumers and backend services. As an API gateway, APIGEE takes care of common functionalities required for the APIs. Hence backend services can concentrate only on the core business logic. The below digram depicts where exactly APIGEE fits.

Now we will see how the request and response goes through APIGEE. Every client request will go through proxy and target endpoints where we will attach APIGEE policies. Policy is nothing but a simple module which will provide common API functionality which we can configure using XML. In each flow APIGEE exposes lot of flow variables.

Below are list of policies available out of the box which you can attach to proxy and target flows.

Apigee MessageLogging policy has limitation to use configurable parameters such as Syslog server host, port, etc. details. If we are not going to configure these parameters we may get into trouble while moving the proxy from one environment to another environment. To achieve the portability the approach is to have MessageLogging policy for each environment. Based on the environment in which the proxy is running the policy will be applied. Below is the sample proxy with message logging policy for each environment. The proxy definition is given below.

The test and prod environment MessageLogging policy configuration is given below.

The proxy demonstrated is available on GitHub to download and play with it.

In this article, we will see how to encode and decode base64 strings while building APIGEE proxies. As part of APIGEE, we have BasicAuthentication policy which deals with base64 encoded authorization header. But if we want to deal with any base64 encoded string other than Authorization header we should go with JavaScript policy or JavaCallout policy or PythonScript policy custom implementation. In this article, I will show you how to achieve base64 encode and decode using JavaScript policy.